Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive, life-threatening systemic disorder that is an underrecognized cause of heart failure (HF). When the diagnosis of wild-type ATTR-CM (ATTRwt-CM) is delayed, patients often undergo additional assessments, deferring appropriate management as symptoms potentially worsen. Prompt recognition of patients at risk for ATTRwt-CM is essential to facilitate earlier diagnosis and disease-modifying treatment. A previously developed machine learning model performed well in identifying ATTRwt-CM in patients with HF vs controls with nonamyloid HF using medical claims/electronic health records, providing a systematic framework to raise disease suspicion. OBJECTIVE: To further evaluate this model’s performance in identifying ATTRwt-CM using a large claims database of older adults with HF and confirmed ATTRwt-CM or nonamyloid HF; and to explore the characteristics and health care resource utilization (HCRU) of patients with confirmed and suspected ATTRwt-CM. METHODS: In this retrospective study, the prior model was applied using Humana administrative claims for patients diagnosed with ATTRwt-CM (cases) and nonamyloid HF (controls [1:1]). Patients were aged 65-89 years, had at least 2 claims for HF diagnosis (2015-2020), and were continuously enrolled in a Medicare Advantage prescription drug plan for at least 12 months before and at least 6 months after HF diagnosis. For the assessment of characteristics and HCRU, the suspected risk level was categorized based on the predicted probability (PP) from model output (high, moderate, and low risk: PP≥0.70; ≥0.50 and < 0.70; and < 0.50, respectively). RESULTS: Of 267,025 eligible patients, 119 (0.04%) had confirmed ATTRwt-CM; of 266,906 patients with nonamyloid HF, 10,997 (4.1%), 68,174 (25.5%), and 187,735 (70.3%) were categorized as high, moderate, and low risk for ATTRwt-CM, respectively. The model demonstrated sensitivity/specificity/accuracy/receiver operating characteristic area under the concentration-time curve of 88%/65%/77%/0.89, respectively, in differentiating ATTRwt-CM from nonamyloid HF. In patients with confirmed ATTRwt-CM, the mean (SD) time between HF and ATTRwt-CM diagnoses was 751 (528) days; 65% and 48% were hospitalized before and after ATTRwt-CM diagnosis, respectively. Atrial fibrillation was more common in patients with confirmed ATTRwt-CM and high risk (39% and 55%) vs low risk (27%). Hospitalization and emergency department visits after HF diagnosis were reported in 57% and 46% of patients with high ATTRwt-CM risk, respectively. CONCLUSIONS: The ATTRwt-CM predictive model performed well in identifying disease risk in the Humana Research Database. Patients at high risk for ATTRwt-CM had high HCRU and may benefit from the earlier suspicion of ATTRwt-CM. The model may be used as a tool to identify patients with a suspected high risk for the disease to facilitate earlier detection and treatment.


OBJECTIVE:
To further evaluate this model's performance in identifying ATTRwt-CM using a large claims database of older adults with HF and confirmed ATTRwt-CM or nonamyloid HF; and to explore the characteristics and health care resource utilization (HCRU) of patients with confirmed and suspected ATTRwt-CM.

METHODS:
In this retrospective study, the prior model was applied using Humana administrative claims for patients diagnosed with ATTRwt-CM (cases) and nonamyloid HF (controls [1:1]). Patients were aged 65-89 years, had at least 2 claims for HF diagnosis (2015-2020), and were continuously enrolled in a Medicare Advantage prescription drug plan for at least 12 months before and at least 6 months after HF diagnosis. For the assessment of characteristics and HCRU, the suspected risk level was categorized based on the predicted probability (PP) from model output (high, moderate, and low risk: PP≥0.70; ≥0.50 and < 0.70; and < 0.50, respectively).

Plain language summary
Older adults with heart failure (HF) at risk for transthyretin amyloid cardiomyopathy (ATTR-CM) may need many visits to the hospital before they receive a correct diagnosis. Earlier suspicion of ATTR-CM may allow earlier diagnosis/treatment and avoid needless hospital visits. In this study, researchers used a new tool in medical claims data to look for people with HF and signs of ATTR-CM who may benefit from early testing for the disease.

Implications for managed care pharmacy
A machine learning predictive model performed well in identifying wild-type ATTR-CM (ATTRwt-CM) risk in older patients with HF in a large administrative claims database. In raising suspicion of ATTRwt-CM and highlighting the need for further confirmatory testing, model use may facilitate earlier detection and treatment. Given the high morbidity and mortality of ATTRwt-CM, and the current availability of disease-modifying therapy, model application in various potential clinical scenarios may play a valuable role in identifying undiagnosed patients in the future.
Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model To help enhance screening for ATTRwt-CM, researchers recently developed and validated a random forest-based machine learning risk prediction model using administrative medical claims data and electronic health records. 25 The model revealed cardiac and noncardiac clinical characteristics associated with ATTRwt-CM that could be used to differentiate the disease from other causes of HF. When validated in large, nationally representative databases, the predictive model identified ATTRwt-CM in patients with HF with high sensitivity, specificity, and accuracy, providing a systematic framework that could be used to raise suspicion of the disease in various settings. In this study, we sought to further evaluate the performance of the machine learning algorithm using claims data of older adults (≥65 years) with HF and a confirmed diagnosis of ATTRwt-CM who were enrolled in a Medicare Advantage prescription drug (MAPD) plan. In addition, we aimed to explore the characteristics and HCRU of patients with confirmed ATTRwt-CM and patients with nonamyloid HF who were at potential risk for ATTRwt-CM. In the latter analyses, we focused particular attention on patients identified as being at high risk for ATTRwt-CM, as this population may achieve the greatest benefit from earlier referral and disease management.

STUDY DESIGN AND PATIENT COHORTS
In this retrospective, observational study, administrative claims data (Humana Research Database) were used to identify patients who had at least 2 claims for an HF diagnosis recorded at least 30 days apart, from October 1, 2015, through June 30, 2020. The most recent date in the date range was selected to minimize the potential impact of the novel coronavirus outbreak on HCRU findings. Eligible patients were aged between 65 and 89 years at the time of their first claim for HF (defined as the index date) and were continuously enrolled in an MAPD plan in the United States for at least 12 months before the index date and at least 6 months after the index date. Patients were followed until the end of enrollment or the end of the observation period (December 31, 2020), hospice admission, or death (whichever occurred first).
This study included 2 main stages. In the first stage, among eligible patients with HF, we identified the ATTRwt-CM case cohort, which included patients who had at least 1 claim with a diagnosis code for ATTRwt-CM (E85.82) after the second claim for HF and had no claims with diagnosis codes for light chain amyloidosis, cerebral amyloid angiopathy, or end-stage renal disease during the study period. Among the remaining eligible patients with HF (ie, those with nonamyloid HF), controls were randomly was more common in patients with confirmed ATTRwt-CM and high risk (39% and 55%) vs low risk (27%). Hospitalization and emergency department visits after HF diagnosis were reported in 57% and 46% of patients with high ATTRwt-CM risk, respectively.

CONCLUSIONS:
The ATTRwt-CM predictive model performed well in identifying disease risk in the Humana Research Database. Patients at high risk for ATTRwt-CM had high HCRU and may benefit from the earlier suspicion of ATTRwt-CM. The model may be used as a tool to identify patients with a suspected high risk for the disease to facilitate earlier detection and treatment.
Infiltration of the myocardium with deposits of extracellular amyloid fibrils formed by misfolded precursor proteins can result in amyloid cardiomyopathy. 1 More than 90% of all amyloid cardiomyopathy is attributed to acquired monoclonal immunoglobulin light chain-related amyloidosis and transthyretin protein-related amyloidosis. 1,2 Transthyretin amyloid cardiomyopathy (ATTR-CM) may arise because of the presence of TTR gene variants but most often develops through the age-related accumulation of wild-type TTR protein. 3,4 Traditionally, ATTR-CM has been considered a rare disease, but the frequency of its occurrence has not been well established. Over the past decade, the estimated prevalence of wild-type ATTR-CM (ATTRwt-CM) in patients with heart failure (HF) with preserved ejection fraction (HFpEF) has ranged from 7% to 17%. 5-7 ATTR-CM is a life-threatening systemic disorder associated with cardiac and noncardiac manifestations; progressive physical disability; quality of life impairment; and high rates of morbidity, mortality, and health care resource utilization (HCRU). 2,[8][9][10][11][12][13] In untreated patients with wild-type and variant disease, postdiagnosis median survival is estimated to be 3.6 and 2.5 years, respectively. 14,15 In older individuals with HF, ATTR-CM is often overlooked as clinicians focus on more common causes of heart disease, such as hypertension or coronary artery disease. [16][17][18] In a large, prospective, observational study of patients with ATTRwt-CM, 42% of patients experienced a delay in diagnosis of more than 4 years after the presentation of cardiac symptoms. 10 Failure to promptly and accurately diagnose ATTRwt-CM can result in inappropriate consultations, assessments, and treatments prior to its diagnosis, as well as worse clinical outcomes after diagnosis and the initiation of appropriate disease-modifying therapy. 16,19,20 Awareness has improved with greater recognition of the diagnostic "red flags" that can prompt suspicion of ATTRwt-CM and the availability of noninvasive imaging techniques that can be used to effectively assess at-risk patients. 18,[21][22][23][24] However, additional screening modalities are needed to enable early identification of patients with HF who may be at risk for this debilitating disease.
Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model selected using the machine learning algorithm to equal the number of ATTRwt-CM cases identified. The ATTRwt-CM case and nonamyloid HF control cohorts were selected in a 1:1 ratio, without matching, to evaluate the machine learning model's performance in distinguishing ATTRwt-CM from nonamyloid HF.
In the second stage, the machine learning model was used to assign a probability of suspected ATTRwt-CM risk in all patients with nonamyloid HF. The patients were categorized by suspected ATTRwt-CM risk level based on 3 thresholds of predicted probabilities (PPs): (1) those suspected to be at high risk for ATTRwt-CM (PP≥0.70; "high risk"); (2) those suspected to be at risk for ATTRwt-CM (PP≥0.50 and < 0.70; "moderate risk"); and (3) those not suspected to be at risk for ATTRwt-CM (PP<0.50; "low risk"). The PP thresholds were selected based on the original research conducted by Huda et al 25 with the random forest-based machine learning risk prediction model.
International Classification of Diseases, Ninth and Tenth Revisions (ICD-9, ICD-10) diagnosis codes were used to code diagnoses and procedures in the Humana claims database to identify patients and evaluate patient characteristics and HCRU in this study. All data used for this study were in an anonymized, structured format, excluding patient personal information. Because the data were not subject to privacy laws according to applicable legal requirements, patient informed consent was not required. The Humana Human Subject Protection Office deemed this study to be exempt from institutional review board approval because of its retrospective nature and use of a limited dataset of administrative claims.

MODEL PERFORMANCE AND PATIENT ASSESSMENTS
The predictive performance of the machine learning model in ATTRwt-CM cases and nonamyloid HF controls was evaluated based on the number of true positives, false positives, true negatives, and false negatives. The following parameters were calculated: sensitivity, specificity, precision (positive predictive value), accuracy, and receiver operating characteristic area under the concentration-time curve (ROC AUC).
Patients' pre-index demographic and clinical characteristics were assessed by ATTRwt-CM risk category. Demographic characteristics included age, sex, race, geographic location by region, population density, and low-income subsidy eligibility/dual eligibility at baseline (definitions in Supplementary Table 1, available in online article). 26,27 Clinical characteristics were evaluated based on pre-index medical and pharmacy claims using the Elixhauser comorbidity index, 28,29 the RxRisk-V score, 30 and frailty index 31 (definitions in Supplementary Table 2).
The prevalence of the individual Elixhauser comorbidities, quantity of unique medications, HF type, and health care provider specialties were also captured from medical and pharmacy claims.
HCRU before and after the index date was assessed based on the outpatient, acute, and post-acute care outcomes identified in medical or pharmacy claims. Outpatient care utilization was computed as the number (%) of patients with claims for all-cause physician encounters, outpatient visits, and specific imaging procedures (ie, pyrophosphate imaging, cardiac biopsy, and cardiac magnetic resonance imaging). Acute care was calculated as the number (%) of patients with claims for all-cause hospitalizations, all-cause readmissions within 30 to 90 days of hospital discharge, and emergency department (ED) visits. The calculation of post-acute care included the number (%) of patients with claims for skilled nursing facilities, home health services, physical/occupational/speech therapy, and medical and social services. Pharmacy use was measured based on the number of unique prescription medications filled.

STATISTICAL ANALYSES
Descriptive analyses were conducted to evaluate patient demographic and clinical characteristics at baseline (ie, up to 12 months before the index date) in patients with confirmed ATTRwt-CM (cases) and those with high, moderate, or low risk for the disease. Continuous variables were characterized by mean values with SD as well as median and interquartile range; categorical variables were characterized by counts and proportions.
After the index date, the time to the diagnosis of ATTRwt-CM and HCRU before and after ATTRwt-CM diagnosis were reported for patients with confirmed ATTRwt-CM. HCRU was reported from the second HF diagnosis until the date of ATTRwt-CM diagnosis and from the time of ATTRwt-CM diagnosis to the end of the study period. The count and proportion of patients with HCRU were reported. Counts less than 10 are suppressed to protect patient privacy and confidentiality. 32 After the index date, HCRU was also assessed in patients at high risk for ATTRwt-CM. A sensitivity analysis of HCRU was conducted in patients in the high-risk cohort who had data available for at least 12 months after HF diagnosis. In the high-risk cohort, logistic regression analysis was performed on the demographic and clinical factors associated with all-cause hospitalizations, ED visits, and any post-acute care services to calculate odds ratios and 95% CIs.
Data management and analyses for this study were performed using the Statistical Analysis Software Enterprise Guide Version 7.15.
Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model

PATIENT DISPOSITION
Among patients satisfying eligibility criteria (n = 267,025), 119 patients (0.04%) had a confirmed diagnosis of ATTRwt-CM based on claims data (Supplementary Figure 1). In these patients, the mean (SD) time between the diagnosis of HF and the diagnosis of ATTRwt-CM was 751 (528)

MODEL PERFORMANCE
In the analysis of model performance, in differentiating ATTRwt-CM from nonamyloid HF, the model correctly identified patients with ATTRwt-CM 88% of the time (sensitivity), with positive predictive value, specificity, and accuracy of 71%, 65%, and 77%, respectively (Table 1).
According to the ROC AUC metric, model predictions were correct 89% of the time.

DEMOGRAPHIC AND CLINICAL CHARACTERISTICS
Numerical similarities and differences were observed in the baseline demographic characteristics of patients with confirmed ATTRwt-CM and in those at high, moderate, and low risk for the disease ( Table 2). The mean age at baseline was similar across the ATTRwt-CM cohorts, ranging from approximately ages 76 to 78 years. A higher proportion of patients with confirmed ATTRwt-CM were men (78%) compared with patients who were at high, moderate, and low risk (53%, 51%, and 48%, respectively). A lower proportion of patients in the confirmed ATTRwt-CM cohort were White (62%) than in the high-, moderate-, and low-risk cohorts (80%, 81%, and 79%, respectively). Lower proportions of patients with confirmed ATTRwt-CM (12%) or high risk (13%) were eligible for lowincome subsidy or had dual eligibility than patients at low risk (24%). Numerical disparities were also seen among patients in the ATTRwt-CM cohorts in certain baseline clinical characteristics ( Table 2). The mean Elixhauser comorbidity index (SD) was 3.1 (2.4) in patients with confirmed ATTRwt-CM vs 4.6 (2.8) in patients at low risk. The RxRisk-V score (SD) was 5.2 (3.6) and 6.6 (3.5) in the confirmed ATTRwt-CM and low-risk cohorts, respectively. In the confirmed ATTRwt-CM group, 42% and 33% of the patients had HFpEF and HF with reduced ejection fraction (HFrEF), respectively, compared with 32% and 20% of those who were at low risk of the disease.

Lower proportions of patients with confirmed
ATTRwt-CM and patients with a high risk of the disease (25% and 24%, respectively) were diagnosed by primary care physicians than patients with low risk (35%). Higher proportions of patients in the confirmed and high-risk cohorts (28% and 17%) were diagnosed by cardiologists than those in the low-risk cohort (11%).
Among the individual Elixhauser comorbidities, 3 were reported in more than 25% of patients across the ATTRwt-CM cohorts: congestive HF, cardiac arrhythmias, and uncomplicated hypertension (Supplementary Table 3). In the moderate-and low-risk cohorts, 3 other comorbidities were reported in more than 25% of patients: chronic obstructive pulmonary disease, uncomplicated diabetes, and renal failure. More than 25% of patients in the low-risk cohort also had claims for peripheral vascular disease and complicated diabetes.

HCRU
Patients With Confirmed ATTRwt-CM. After the first HF diagnosis was recorded, the proportion of patients with claims for all health care resources, except outpatient visits and skilled nursing facility use, was higher before their diagnosis of ATTRwt-CM was confirmed than after it was confirmed ( Figure 1). The proportion of patients with outpatient visit claims remained high (> 99%) before and after ATTRwt-CM diagnosis. The proportion of patients with all-cause hospitalization claims decreased from 65% of patients over an average of 751 days before ATTRwt-CM diagnosis to 48% of patients over an average of 327 days after diagnosis, whereas those with claims for hospital readmission for any reason decreased from 46% to 26% and ED visits decreased from 60% to 45%. The proportion of patients with claims for skilled nursing facility care increased slightly after ATTRwt-CM diagnosis (from 11% to

Performance of the Machine Learning Model in Patients With Confirmed
Wild-Type Transthyretin Amyloid Cardiomyopathy (Cases)/Heart Failure (Controls)

TABLE 1
Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model In patients with confirmed ATTRwt-CM, the proportion with claims for several health care resources increased from the year 2 years before ATTRwt-CM diagnosis to the year before diagnosis ( Figure 2). All-cause hospitalizations 13%), but other post-acute care utilization decreased (from 65% to 58%). The proportion of patients who had at least 1 prescription filled also decreased, from 93% before to 45% after ATTRwt-CM was diagnosed.   Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model In the sensitivity analysis, the proportion of patients at high risk for ATTRwt-CM who had claims for acute and post-acute care services consistently increased after their HF diagnosis was first recorded (Supplementary Figure 2). The proportion of patients with at least 1 hospitalization claim increased from 24% to 65% before and after HF diagnosis. Before and after the HF diagnosis, 29% and 48% of patients, respectively, had claims for hospital readmission, and 36% and 58% for an ED visit.
Multiple factors were associated with HCRU among patients identified as being at high risk for ATTRwt-CM by the model.  Figure 3).
increased from 35% over an average of 212 days in the year 2 years before diagnosis to 46% over an average of 305 days in the year before diagnosis. All-cause readmissions rose from less than 12% (data suppressed because the patient count was < 10) to 23% in this time span, and ED visits rose from 35% to 50%.

Patients at High Risk for ATTRwt-CM.
Rates of acute care resource utilization were higher in patients at high risk for ATTRwt-CM than in patients with confirmed ATTRwt-CM for an average of 327 days after they received their ATTRwt-CM diagnosis (Figure 3). In the high-risk cohort, claims for allcause hospitalization, all-cause readmission, and ED visits were found in 57%, 37%, and 46% of patients, respectively. In the confirmed ATTRwt-CM cohort, the corresponding rates were 48%, 26%, and 45% of patients, respectively.
Differences in mean period duration should be considered when comparing health care resource utilization rates between cohorts. a Counts were less than 10 and were suppressed to protect privacy and confidentiality. 32

FIGURE 2 Proportions of Patients in the Confirmed ATTRwt-CM Cohort With Claims for Health Care Resources 1 and 2 Years Before and After the ATTRwt-CM Diagnosis
Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model 50 years based on ICD diagnosis codes in US medical claims data from IQVIA and electronic health record data from Optum. 25 When its performance was validated using the l atter data sources, the model demonstrated high se nsitivity, specificity, accuracy, and ROC AUC: 87%, 87%, 87%, and 0.93, respectively, in IQVIA; and 90%, 79%, 84%, and 0.95, respectively, in Optum databases. In the current study, we assessed the performance of the machine learning al gorithm among older adults who were ages 65 to 89 years, had HF, and were enrolled in an MAPD plan. In this older population, we found the model had similar sensitivity (88%), but lower specificity (65%) and accuracy (77%). These disparities may be explained in part by differences in the diagnosis codes (ICD-9/10) and datasets (administrative and electronic health records) used in the prior validation work vs our research.
In the current study, we also evaluated the demographic/clinical characteristics and HCRU for patients  Figure 5).

Discussion
The current study was conducted as part of a large-scale research initiative to identify patients with HF who were at risk for ATTRwt-CM using a machine learning predictive model. The original random forest-based model was developed to recognize phenotypic patterns suggestive of cardiac amyloidosis in patients with HF older than age

FIGURE 3 Health Care Resource Utilization in Patients Suspected at High Risk for ATTRwt-CM and Patients
With Confirmed ATTRwt-CM Before and After ATTRwt-CM Diagnosis elected en rollment in other health plans. Moreover, the regional imbalance among individuals enrolled in a Humana MAPD plan is noteworthy, as approximately two-thirds and onefifth of patients analyzed in this study were from the South and Midwest, respectively. The relatively small number of patients in the ATTRwt-CM case and nonamyloid HF control cohorts (n = 119 for each) should also be considered when interpreting the results of the current study. The mean duration of the prediagnosis and postdiagnosis periods differed between cohorts in the HCRU analyses, potentially affecting the results. Because of the retrospective nature of the data, relationships may be established based on temporality but not causality. Although our analyses were adjusted, we did not include certain confounding factors, which may have introduced bias. Like the original model, our algorithm predicted ATTRwt-CM risk based on ICD codes readily available in claims data. Without access to patient charts, we could not include clinical information not captured with an ICD code. The confirmed ATTRwt-CM cohort was identified based on an ICD-10 code first available in October 2017. We identified patients with claims for HF between October 2015 and June 2020 who were followed for at least 6 months. We were unable to flag some patients with HF in claims data and classify them as confirmed ATTRwt-CM cases, including patients who were identified between October 2015 and March 2017 with less than 6 months' follow-up and an ATTRwt-CM diagnosis, and patients who received more general cardiac amyloidosis ICD codes.
Moreover, patients were required to be enrolled 1 year prior to the index date and at least 6 months after the index date. ATTRwt-CM was confirmed and HCRU evaluated during the follow-up period, a potentially than that in patients with confirmed ATTRwt-CM postdiagnosis. In the sensitivity analysis evaluating HCRU the year before and after diagnosis of HF, the proportion of patients with claims for acute and post-acute care services increased during follow-up, with hospitalizations more than doubling.
In our analysis of factors that influenced HCRU, patient age appeared to have an impact only on the frequency of claims for post-acute care services, whereas women had a higher risk of hospitalization, ED visits, and postacute care services than men. Patients who were Black had lower odds for hospitalization (16%) but higher odds for ED visits (37%) than those who were White. Although patient history of some cardiac conditions, such as unstable angina, HFpEF/ HFrEF, and syncope, was found to increase the risk for HCRU, others, such as atrial fibrillation, heart block, and ca rdiomyopathy, were shown to decrease the risk. Although further exploration of these findings is warranted, they may be attributable to differences in the management of these conditions.

LIMITATIONS
The strengths and limitations of the original random forest predictive model have been previously discussed. 25 A strength of our analysis is the size of the database, as Humana is one of the largest MAPD health plan providers in the United States. Several limitations of the current study should also be considered, including shortcomings related to administrative claims data use, eg, potential errors in coding and missing data. In addition, because we used data from a US population of older adults (aged 65-89 years) who elected enrollment in a Humana MAPD plan, the results of our analysis may not be generalizable to patients outside of the United States, younger patients, or patients who have with HF in the confirmed ATTRwt-CM cohort and high-, moderate-, and low-risk cohorts. We observed some differences in patient characteristics, including, most notably, that more patients in the confirmed ATTRwt-CM cohort were men and fewer were White than patients in the high-, moderate-, and low-risk cohorts. Patients in the confirmed ATTRwt-CM cohort had lower comorbidity scores and a higher proportion had HFpEF than patients in the high-, moderate-, and low-risk cohorts. These findings suggest that certain groups of patients with HF, eg, those who are female, non-White, and without an HFpEF diagnosis, those who are at high risk for ATTRwt-CM and not yet diagnosed, are potentially being overlooked. This possibility supports a need for additional research and efforts to educate health care providers and raise their awareness of populations at risk for ATTRwt-CM.
On average, patients were diagnosed with ATTRwt-CM almost 2 years after their HF diagnosis. The proportion of patients with claims for hospitalization, hospital readmission, and ED visits decreased by at least 25% after their amyloidosis diagnosis compared with before their diagnosis, whereas rates of filled prescriptions decreased by more than 50%. These findings suggest that, although patients were found to have a serious cardiovascular condition, the intensity of care decreased once the etiology was accurately identified, as it allowed for focused management of their disease.
In patients with confirmed ATTRwt-CM, the proportion with claims for most health care resources increased each year prior to the diagnosis of ATTRwt-CM. In patients suspected to be at high risk for ATTRwt-CM, the proportions of patients with claims for acute care services were lower than that in patients with confirmed ATTRwt-CM prediagnosis, but higher biased approach because of the lack of information after patients withdrew from the plan. The required 6-month enrollment after HF diagnosis was likely inadequate to verify a nonamyloid diagnosis. In our sensitivity analysis conducted in a subset of patients with 12 months' follow-up, we found that more than 90% of the patients suspected to be at high risk for ATTRwt-CM had at least 12 months of follow-up enrollment. Given the expanded follow-up period from 6 to 12 months, the proportion of patients with claims for HCRU increased.

Conclusions
A risk prediction model for ATTRwt-CM in patients with HF performed well in a population of patients enrolled in a Humana MAPD, with performance levels approaching those reported elsewhere. 25 These findings suggest that the algorithm may be used as a tool to identify patients with suspected ATTRwt-CM or, at a minimum, patients at high risk of the disease, to facilitate earlier referral, detection, and treatment. In general, HCRU proved to be highest in the year leading up to the diagnosis of ATTRwt-CM, as compared with 1-2 years before diagnosis or after diagnosis, although the use of post-acute care services (eg, home health care) was substantially higher after diagnosis. Additional research is needed to better understand the impact of earlier ATTRwt-CM diagnosis on HCRU in patients with HF suspected to be at high risk for the disease.

DISCLOSURES
This study was sponsored by Pfizer.
Medical writing support was provided by Donna McGuire of Engage Scientific Solutions and funded by Pfizer.
Drs Bruno and Schepart and Mr Casey are currently employees of Pfizer and equity holders in this publicly traded company. Dr Reed was an employee of Pfizer at the time that this analysis was planned